Due to rapid growth of digital communication attributed to advancement of Internet technologies, at every time instant huge amount of multimedia information is created and transmitted on the Internet. However, ownership identification, copyright violation, and identity theft are proving as the serious concerns because of the malicious attacks or hacking of open information. Attackers can alter or delete the information, and claim the ownership or prevent the receiver to get the data. Hence, researchers try to address such challenges. One such effort is concerned with multimedia data hiding through Steganography. In this article, a polynomial sequence generator called Lah Transform (LhT) is applied for developing a Steganography method. LhT generates an integer polynomial sequence in coefficient form by evaluating the pixel values using addition and multiplication. Initially, the cover image is partitioned into nonoverlapping b‐pixel groups (where, b = 3 or 4) which in succession are converted into transform domain using LhT in row‐major order. Secret bits are embedded into the LhT coefficients in varying proportions to achieve variable payload. Coefficient adjustment followed by the embedding process ensures minimum quality distortion. Inverse LhT is applied to regenerate b‐pixel groups in the spatial domain. Results assure that incongruity between the cover pixels and stego‐pixels rises as the value of b increases. Hence, to achieve a high payload, smaller values of b are chosen. The proposed method yields higher peak signal to noise ratio values and payload compared to some recent methods found in the literature. Code of our method is available here.
Breast cancer has become a common malignancy in women. However, early detection and identification of this disease can save many lives. As computer-aided detection helps radiologists in detecting abnormalities efficiently, researchers across the world are striving to develop reliable models to deal with. One of the common approaches to identifying breast cancer is through breast mammograms. However, the identification of malignant breasts from mass lesions is a challenging research problem. In the current work, we propose a method for the classification of breast mass using mammograms which consists of two main stages. At first, we extract deep features from the input mammograms using the well-known VGG16 model while incorporating an attention mechanism into this model. Next, we apply a meta-heuristic called Social Ski-Driver (SSD) algorithm embedded with Adaptive Beta Hill Climbing based local search to obtain an optimal features subset. The optimal features subset is fed to the K-nearest neighbors (KNN) classifier for the classification. The proposed model is demonstrated to be very useful for identifying and differentiating malignant and healthy breasts successfully. For experimentation, we evaluate our model on the digital database for screening mammography (DDSM) database and achieve 96.07% accuracy using only 25% of features extracted by the attention-aided VGG16 model. The Python code of our research work is publicly available at: https://github.com/Ppayel/BreastLocalSearchSSD.
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